Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation
We present an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries. The system converts user intent into structured API calls, enabling efficient access to satellite imagery and environmental datasets. The architecture integrates three agents: Guardrail for safety and policy enforcement, General-QA for intent interpretation, and Recommender-Analyst for schema-aware API call generation. This coordinated design ensures reliab
Record details
Published: 13 June 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (13 June 2026), “Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation,” evidence record 1035, https://ethics.ai/record/1035 (originally published by arXiv).
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